SNP-based analysis reveals high genetic structure and diversity in umbu tree (Spondias tuberosa Arruda), a native and endemic species of the Caatinga biome

SNP-based analysis reveals high genetic structure and diversity in umbu tree (Spondias tuberosa Arruda), a native and endemic species of the Caatinga biome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article SNP-based analysis reveals high genetic structure and diversity in umbu tree (Spondias tuberosa Arruda), a native and endemic species of the Caatinga biome Wellington Ferreira do Nascimento, Flaviane Malaquias Malaquias Costa, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4253622/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 May, 2024 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted 9 You are reading this latest preprint version Abstract Umbu ( Spondias tuberosa Arruda) is an endemic fruit tree restricted to the Brazilian seasonally dry tropical forest called Caatinga. This study aimed to evaluate the structure and genomic diversity of umbu trees from seven locations in the Caatinga biome, distributed among four Brazilian states. Using genotyping-by-sequencing (GBS), a total of 5,336 SNPs were obtained, of which 250 showed outlier behavior. Therefore, 5,086 neutral SNPs were used for population structure and genetic diversity analyses. Both discriminant analysis of principal components (DAPC) and neighbor-joining cluster analyses classified the accessions into four groups, with a genetic structure observed among groups, disagreeing with our initial hypothesis of low genetic structure between locations. Isolation by distance (r 2 = 0.974; p = 0.0015) was detected. Moderate to high levels of genetic diversity were found, with the average observed heterozygosity ( H O = 0.221) higher than the expected heterozygosity ( H E = 0.199) and with negative inbreeding coefficient ( F IS ) values. Most genetic variation was found within locations, although high diversity between locations (22.1%) was observed. The results obtained are important for understanding the levels and distribution of genetic variation, suggesting that most locations are priorities for conservation actions, contributing with different alleles to the species' gene pool in Brazil. Genetic diversity population structure Caatinga biome native fruit endemic species Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Human activities have impacted all ecosystems on our planet, reducing their biodiversity and, consequently, their ability to maintain ecological functions and provide benefits to society (Haddad et al. 2015; Newbold et al. 2015; Miraldo et al. 2016). The Brazilian seasonally dry tropical forest called Caatinga is one of the most threatened biomes in the country due to the poorly planned use of its resources, especially concerning the removal of native vegetation (Santana and Souto 2006). Caatinga vegetation includes several endemic species, such as Spondias tuberosa Arruda (Anacardiaceae), popularly known as umbu tree (Figs. 1 a and 1 b) (Souza 2000; Lins Neto et al. 2010, 2013; Mitchell and Daly 2015). Its popular name is derived from the Tupi-Guarani indigenous word “ ymb-u ”, which means “the tree that gives water” (Epstein 1998). This results from a physiological adaptation of the plant forming roots with xylopods (Figs. 1 e and 1 f) capable of accumulating water, minerals, and organic solutes (Epstein 1998; Cavalcanti et al. 2010 ), which allows their survival in the dry season (Silva et al. 2008; Cavalcanti et al. 2010 ). Umbu is an incipiently domesticated deciduous fruit tree (Lins Neto et al. 2013, 2014). Although it is native to the Caatinga, it occurs frequently in areas near the Atlantic Forest (Balbino et al. 2018 ), from the north of Minas Gerais State in the Southeast region to the most northern point of the Northeastern region in Brazil (Santos 1997). In this semi-arid region, this fruit tree represents an important food and medicinal resource for local residents, in addition to having high market potential (Albuquerque et al. 2007 ; Siqueira et al. 2016; Mertens et al. 2017; Cordeiro et al. 2018). The exploitation of its fruits (Fig. 1 d) is mainly based on extractivism (Mertens et al. 2017), directly proportional to the flavor (if bittersweet), size, and quantity of pulp (Lins Neto et al. 2010), as they are commercially exploited for “ in natura ” consumption and preparation of juices, jellies, ice creams, sweets, and frozen pulp (Mertens et al. 2017). The fruits and leaves are also used as fodder for small domestic mammals such as sheep and goats (Cavalcanti et al. 2004). In traditional medicine, different parts of the plant have been used to treat venereal diseases, digestive disorders, diarrhea, diabetes, menstrual disturbances, and placental delivery (Albuquerque et al. 2007 ; Siqueira et al. 2016; Cordeiro et al. 2018). Siqueira et al. (2016) reported evidence of an anti-inflammatory action using the leaves, suggesting potential therapeutic benefits for inflammatory conditions. The pharmacological potential of the leaf extract as an antioxidant and antifungal agent was also demonstrated by Cordeiro et al. (2018). S. tuberosa is an andromonoecious species with gametophytic self-incompatibility (Leite and Machado 2010), pollinated mainly by bees and wasps (Nadia et al. 2007; Almeida et al. 2011 ). It is a predominantly allogamous species, with an estimated outcrossing rate of 74% (Souza 2000), and variation between 80.4% (multi-locus) and 84.1% (single locus) (Santos and Gama 2013). However, fruit production is low, considering the high number of flowers produced (Fig. 1 c). According to Stephenson (1981), this may be related to extrinsic factors, such as limiting environmental resources, and it may also be related to intrinsic factors, such as zoochoric fruits of high energy value aborted at a young age (Nadia et al. 2007). In the Caatinga, the exploration of extensive pastures is the predominant anthropic disturbance (Alves et al. 2009 ) and combined with the destruction of their habitat (Mertens et al. 2017; Balbino et al. 2018 ), induces a reduction of the umbu tree populations, which may compromise the genetic diversity of the species (Mitchell and Daly 2015). It is supposed that much of the existing genetic variability of S. tuberosa has been lost due to indiscriminate deforestation, rapid advance of agricultural frontiers for the plantation of exotic crops, and urban expansion in their respective areas of occurrence, which may have been enhanced by natural threats, such as climate change (Mertens et al. 2017). Therefore, understanding the species' genetic diversity is essential to rationalize the use of its genetic resources, elaborate efficient conservation strategies, and develop genetic improvement programs (Souza et al. 2016). Several molecular markers have been used to assess the genetic diversity of this species, such as RAPD (Random Amplified Polymorphic DNA) (Moreira et al. 2007), AFLP (Amplified Fragment Length Polymorphism) (Santos et al. 2008; Santos and Oliveira 2008; Santos et al. 2011), ISSR (Inter-simple Sequence Repeat) (Lins Neto et al. 2013), and SSR (Simple Sequence Repeat) (Balbino et al. 2018 , 2019 ; Santos et al. 2021a,b). Only one study was found using SNP (Single Nucleotide Polymorphisms) markers in S. tuberosa (Nobre et al. 2018) addressing the hybrid origin of S. bahiensis P. Carvalho, van Den Berg & M. Machado. The genotyping-by-sequencing (GBS) technique is based on the complexity reduction of genomic DNA by restriction enzymes and on the use of barcode DNA adapters to produce multiplexed libraries of samples that are submitted to next-generation sequencing (NGS) (Poland and Rife 2012). With this combination, the technique has demonstrated the ability to produce thousands of SNPs in several species, including fruit trees (Goonetilleke et al. 2018). SNP markers are the most abundant genetic polymorphisms in the genome. In addition to the evaluation of neutral variation, they enable the study and identification of regions of the genome that might be under natural selection in the population (outlier loci), that is, regions possibly associated with adaptation (Luikart et al. 2003; Cortinovis et al. 2020; Alves-Pereira et al. 2020 , 2022 )⁠. The present study aimed to assess the genomic diversity and structure of umbu trees ( S. tuberosa ) from seven locations in the Caatinga biome by SNP markers obtained through the GBS technique. Two complimentary hypotheses were tested: a) because S. tuberosa is an endemic species of the Caatinga, predominantly allogamous, presenting gametophytic self-incompatibility, we expected to find low genetic structure between locations; b) the species is in a state of genetic vulnerability, highly threatened by anthropogenic activities, showing a reduction in its populations and, consequently, in its genetic diversity. Material and methods Sampling, DNA extraction, and quantification In the Caatinga biome, the umbu tree occurs in areas of native vegetation but mainly in anthropized ones such as those currently cultivated, pasturelands, homegardens and areas of regeneration of native vegetation after being abandoned for agricultural use. In these areas, young leaves were sampled from a total of 71 umbu trees in seven locations (Table 1 : Fig. 2 ): in Minas Gerais State (MG), between the municipalities of Espinosa and Monte Azul, named Espinosa in this study, in cultivated areas and areas of regeneration of native vegetation; in Bahia State (BA), between the municipalities of Jaguarari and Senhor do Bonfim, named as Senhor do Bonfim in this study, in anthropized remnant fragments; in Pernambuco State (PE), between the municipalities of Lagoa Grande and Santa Maria da Boa Vista, named as Lagoa Grande in this study, in cultivated areas and in areas of regeneration of native vegetation; and four areas in Paraiba State, in the municipalities of São Vicente do Seridó and Queimadas, in homegardens and agriculture cultivated areas; Boqueirão, only in agriculture cultivated areas; and Cabaceiras, in areas of natural vegetation and homegardens, with cattle and goat farming). It was established that the minimum distance among sampled individuals was 500 m, and the maximum was 3,000 m. Table 1 Sampling locations of umbu trees ( Spondias tuberosa ), from seven locations distributed in the states of Minas Gerais (MG), Bahia (BA), Paraíba (PB), and Pernambuco (PE), in the Caatinga biome, including number of individuals (N) sampled, geographic coordinates and climate data ( https://koppenbrasil.github.io/ ). Locations N Latitude Longitude Mean Annual Temp. ( o C) Annual Rain (mm) Altitude (m) Koppen a Espinosa-MG * 3 15°05’27.5”S 42°47’49.0”W 22.2 798.3 675.5 As Senhor do Bonfim-BA * 19 10°18’23.0”S 40°09’44.0”W 23.8 679.7 498.1 As São Vicente do Seridó-PB 3 06°53’40.2”S 36°24’12.3”W 22.9 455.7 573.8 BSh Queimadas-PB 7 07°26’08.4”S 35°53’12.4”W 23.7 639.0 407.1 As Boqueirão-PB 6 07°27’19.2”S 36°06’1.5”W 23.7 466.9 424.5 BSh Cabaceiras-PB 13 07°13’22.3”S 35°53’41.3”W 23.6 429.3 444.1 BSh Lagoa Grande-PE * 20 08°57’27.0”S 40°11’40.0”W 24.5 525.3 429.9 BSh 71 a Koppen classification: As (Tropical savannah: warm, with winter and autumn rains); BSh (Semi-arid: hot and dry, with winter rains). The extraction of genomic DNA was performed using the protocol described by Inglis et al. (2018) with modifications, including three to four prewashes with sorbitol buffer [100 mM Tris-HCl pH 8.0, 0.35 M Sorbitol, 5 mM EDTA pH 8.0, 1% (w/v) Polyvinylpyrrolidone (average molecular weight 40,000; PVP-40)]. The quantification and analysis of DNA quality were performed through electrophoresis in a 1% agarose gel (w/v) stained with Gel Red (Biotium). DNA was quantified based on the phage λ molecular size standards (Invitrogen) at different concentrations (20, 50, and 100 ng µL − 1 ) and validated with a Qubit4 fluorometer (Invitrogen). After quantification, DNA samples were normalized to a 20 ng/uL concentration for GBS library preparation. Genomic library and SNPs identification The genomic library was prepared following the protocol described by Poland et al. (2012). Briefly describing, high-quality genomic DNA (140 ng per sample) was digested at 37°C for 12 hours using a combination of a PstI rare-cutting enzyme (NEB-New England Biolabs) with a MseI frequent-cutting enzyme (NEB-New England Biolabs). The fragments generated from the digestion for each sample were ligated to adapters containing specific barcode sequences using the enzyme DNA T4 ligase and grouped together in a 96-plex. The multiplex was column purified and amplified for PCR enrichment, and it is being submitted to a new purification step. The library was then qualitatively evaluated using the BioAnalyzer system (Agilent Technologies) and quantified using the NEBNext® Library Quant Kit for Illumina (New England Biolabs) on the CFX 384 Touch Real Time PCR Detection System (Bio-Rad Laboratories). Subsequently, the library was sequenced in a flowcell using a sequencer on the HiSeq2500 Illumina platform, with the company EcoMol, at the Genomics Center of ESALQ/USP. The overall sequencing quality was assessed with FastQC (Andrews 2010 ), and the removal of low-quality sequences containing adapters and trimming of sequences to 80 bases was performed with Trimmomatic 0.39 (Bolger et al. 2014 ). De novo identification of SNP markers was performed with Stacks-1.42 (Catchen et al. 2011 ). Sequence demultiplexing for each sample and checking the integrity of restriction sites was performed with the process_radtags module. The initial assembly of loci for each sample was performed with the ustacks module with the parameters of minimum sequencing depth (-m) of 3x and maximum distance allowed between sequences of the same locus (-M) of 2 bases. A catalogue of loci was obtained with the cstacks program allowing a maximum distance between loci of different samples (-n) of 2 bases. The sstacks module was used to compare the loci of each sample with the catalogue loci, and the rxstacks module was used to remove the loci with a lower probability (--lnl_lim − 10). The populations module was used for final data filtering, considering SNP markers as the loci with a minimum sequencing depth of 5x, frequency of the rarest allele (MAF) ≥ 0.01, presence of the SNP in at least 90% of the samples of each one of the sampled locations, and retaining only one SNP per sequenced tag. Sequencing quality metrics of SNP markers were obtained with VCFtools 0.1.17 (Danecek et al. 2011). Statistical analyses Identification of possible outlier loci The identification of possible outlier loci was performed considering the sampled locations. Three complementary tests were performed: Pcadapt (Luu et al. 2017) in which the outlier loci are associated with the genetic groups observed in a principal component analysis (PCA); FstHet (Flanagan and Jones 2017) for identifying loci with excessive high or low F ST values ​​in relation to a neutral distribution, and BayeScan (Foll and Gaggiotti 2008), a Bayesian analysis for estimating posterior probabilities to verify whether or not each locus reflects selection. The pcadapt analysis was performed considering the first four principal components, which suggested great agreement between genetic groups and sample locations. In this analysis, SNP markers with q-values ​​< 0.1 were considered as outliers. The fstHet analysis was performed based on the beta hat estimate (Cockerham and Weir 1993) (analogous to Wrigth’s F ST ), considering as outliers the SNP markers above or below a 95% confidence interval constructed based on 1000 bootstraps. The above analyses were performed with the R packages (R Core Team 2018), pcadapt (Luu et al. 2017) and fsthet (Flanagan and Jones 2017). BayeScan 2.1 (Foll and Gaggiotti 2008) was used to perform 20 pilot runs, with 100000 iterations each, followed by 250000 burn-in steps and 25000 steps with intervals of 50 (total of 1500000 iterations). It was considered in the model that the probability of including selection was 3x lower than that of not including selection. In this analysis, SNP markers with FDR < 0.05 were considered outliers. False positives are frequent in the detection of outlier loci (Luikart et al. 2003). For this reason, the final set of outlier markers consisted of the loci identified in at least two of the three applied tests, as suggested by Luikart et al. (2003) and considered by Alves-Pereira et al. ( 2020 , 2022 ). Statistical analyses carried out with neutral SNP loci The genetic structure and diversity analyses were performed with neutral SNPs, excluding the outlier loci according to the criterion described above. Discriminant analysis of principal components (DAPC) was performed with the adegenet package (Jombart 2008) in the R program (R Development Core Team 2018). The number of clusters from the DAPC was calculated by the K-means method, which runs different probabilities of cluster numbers. By using this method, two groups were detected whereas group I included the locations from the States of Bahia, Pernambuco, and Minas Gerais, while group II included the four locations of Paraíba State (Figs. S1 and S2). The DAPC was also performed using the locations as a priori groupings. The K-means method retained only one principal component that explained 12.4% of the total variation. In the DAPC analysis based on locations, six principal components were retained, of which the first three explained 26.7% of the variation. Therefore, the analyses were continued based on the locations, as they summarized a greater percentage of the genetic variation. The genetic relationship between the samples was performed by cluster analysis with the neighbor-joining method, using Nei’s genetic distances (Nei 1978) performed with the ape package (Paradis and Schliep 2019) in the R program (R Development Core Team 2018). The dendrogram was edited with FigTree v.1.4.3 ( http://tree.bio.ed.ac.uk/softwere/figtree/ ). Pairwise F ST matrices among locations and among the groups delimited by DAPC were calculated with the poppr package (Kamvar et al. 2014) in the R program (R Development Core Team 2018). The genetic diversity parameters for the sampled locations of the total number of alleles ( A ), observed heterozygosity ( H O ), and expected heterozygosity ( H E ), in addition to the inbreeding coefficient ( f ), were estimated using the hierfstat (Goudet and Jombart 2020) and poppr (Kamvar et al. 2014) packages in the R program (R Development Core Team 2018). The distribution of genetic variability between and within locations was detected using the analysis of molecular variance (AMOVA) with the hierfstat (Goudet and Jombart 2020) and poppr (Kamvar et al. 2014) packages in the R program (R Development Core Team 2018). To verify the existence of isolation by distance, the Mantel test was performed with the ade4 package (Dray and Dufour 2007; Dray et al. 2007; Bougeard and Dray 2018 ; Thioulouse et al. 2018), aiming to evaluate the correlation between the genetic divergence from the F ST values ​​of the pairwise matrix between locations and the geographic distances (km), generated from the geographic coordinates, obtained with the geodist package (Padgham and Sumner 2020). A second Mantel test was performed with only the four populations of Paraíba, geographically located closer to each other. The significance level was considered based on 20000 permutations. Results SNP detection and outlier SNPs loci A total of 5,336 SNP markers were identified for 71 samples (mean sequencing depth = 55.4x; standard deviation = 33.3; mean of 0.54% missing data). Of these, 1,624 SNPs were identified as outlier markers (1,029 by the pcadapt program, 468 by the fsthet program, and 127 by the BayeScan program) (Fig. S3 ). The final set of outlier markers consisted of 250 SNPs identified by at least two of the three tests performed (Fig. S3 ). Therefore, the analyses of genetic diversity and population structure were performed with 5,086 SNPs considered neutral, excluding from the total the 250 SNPs identified as outlier loci. Genomic structure among umbu tree locations Six main components were retained for the DAPC based on locations, of which the first three explained 26.7% of the variation (Fig. 3 a; Fig. 3 b). A strong structure was found among the umbu tree samples from different locations, especially among the sampled states (Fig. 3 c), establishing an optimal number of groups corresponding to four groups (Fig. 3 a; Fig. 3 b). The locations of Espinosa-MG, Senhor do Bonfim-BA, and Lagoa Grande-PE formed three isolated groups, all genetically different from each other. As for the four populations collected in Paraíba, all geographically close to each other (Fig. 2 ), there was an overlapping of genotypes suggesting greater genetic similarity among them. The neighbor-joining dendrogram based on Nei's genetic distances (1978) clustered the samples into four groups: group I, consisting of individuals from Lagoa Grande-PE; group II, consisting of individuals from Senhor do Bonfim-BA; group III, consisting of individuals from Espinosa-MG; and group IV grouping the individuals from the four locations sampled in Paraíba, with the individuals from São Vicente do Seridó-PB being the most divergent in relation to the individuals from the other locations (Fig. 4 ). The Mantel test showed a high and significant correlation between genetic distances ( F ST ) and geographic distances (km) (r 2 = 0.974; p = 0.0015) between pairs of locations (Fig. 5 a). However, when the Mantel test was conducted with only the four locations from Paraíba (Fig. 5 b) the result was non-significant (r 2 = 0.575; p = 0.212), which was already expected due to their greater geographical and genetic proximity (Figs. 2 , 3 and 4 ). Based on F ST estimates (Table 2 ), Espinosa-MG is genetically the most distinct from the other locations, with F ST values ​​ranging from 0.202 (Espinosa-MG and Lagoa Grande-PE) to 0.330 (Espinosa-MG and Boqueirão-PB). The locations of Senhor do Bonfim-BA and Lagoa Grande-PE are the next more genetically distant from the others. However, both are genetically closer to each other. The locations in Paraíba are genetically closer to each other, with the F ST values ​​ranging from 0.028 (Boqueirão and Cabaceiras) to 0.074 (São Vicente and Queimadas). The AMOVA showed that most of the observed genetic variation was found within locations (77.9%) (Table 3 ). However, the variation among locations ( F ST = 0.221) is high (Hartl and Clark 2007) and significant, corroborating the results observed in the DAPC and in the dendrogram (Figs. 3 and 4 ). Table 2 F ST estimates between pairs of locations (lower diagonal) and respective 95% confidence intervals (upper diagonal) for the umbu trees ( Spondias tuberosa ) locations. Senhor do Bonfim-BA São Vicente-PB Queimadas-PB Boqueirão-PB Cabaceiras-PB Lagoa Grande-PE Espinosa-MG Senhor do Bonfim-BA (0.113:0.131) (0.149:0.164) (0.144:0.160) (0.143:0.156) (0.047:0.053) (0.198:0.218) São Vicente-PB 0.123 (0.065:0.083) (0.049:0.067) (0.032:0.046) (0.091:0.108) (0.283:0.308) Queimadas-PB 0.157 0.074 (0.051:0.066) (0.041:0.051) (0.127:0.140) (0.315:0.340) Boqueirão-PB 0.152 0.058 0.059 (0.024:0.033) (0.121:0.135) (0.318:0.342) Cabaceiras-PB 0.149 0.039 0.046 0.028 (0.115:0.127) (0.310:0.334) Lagoa Grande-PE 0.050 0.100 0.134 0.128 0.121 (0.193:0.211) Espinosa-MG 0.208 0.295 0.327 0.330 0.322 0.202 Table 3 Analysis of molecular variance (AMOVA) based on 5,086 SNPs used to identify sources of genetic variability between and within sampled locations of umbu trees ( Spondias tuberosa ). Source of variation DF a SS MS % variation PhiST p-value Among locations 6 12822.9 2137.1 22.1 0.221 0.00005 Within locations 64 37242.1 581.9 77.9 Total 70 50065.0 715.2 a DF = degrees of freedom, SS = sum of squares, MS = mean squares, PhiST = estimate analogous to F ST . Genetic diversity for the umbu tree locations In the analysis of genetic diversity for each location (Table 4 ), the total number of alleles ranged from 7,231 (São Vicente-PB) to 9,402 (Lagoa Grande-PE), with an average of 8,136.7 alleles. All locations presented polymorphism greater than 71%, and allelic richness ranged from 1.352 (São Vicente-PB) to 1.538 (Lagoa Grande-PE). Results show moderate to high levels of genetic diversity for the species, with an excess of heterozygotes in all locations except for Lagoa Grande-PE. This can be evidenced by the negative and close to zero inbreeding coefficients (mean F IS = -0.117). The locations of Espinosa-MG, Lagoa Grande-PE and Senhor do Bonfim-BA stand out as having the highest heterozygosities, while the four locations in Paraíba (PB) State showed the lowest heterozygosities. Table 4 Genetic diversity parameters and inbreeding coefficient for umbu trees ( Spondias tuberosa ) locations evaluated with 5,086 SNPs. Locations N a A P ( % ) Ar H O H E F IS F IS CI95% Espinosa-MG 3 7,787 76.6 1.453 0.294 0.215 -0.364 -0.787:-0.120 Senhor do Bonfim-BA 19 9,257 91.0 1.528 0.244 0.244 -0.002 -0.029: 0.019 São Vicente-PB 3 7,231 71.1 1.352 0.198 0.162 -0.225 -0.801:-0.027 Queimadas-PB 7 7,641 75.1 1.371 0.185 0.171 -0.080 -0.203:-0.021 Boqueirão-PB 6 7,581 74.5 1.369 0.189 0.171 -0.107 -0.287:-0.030 Cabaceiras-PB 13 8,058 79.2 1.403 0.193 0.185 -0.043 -0.084:-0.018 Lagoa Grande-PE 20 9,402 92.4 1.538 0.246 0.247 0.004 -0.017: 0.019 Mean - 8,136.7 80.0 1.431 0.221 0.199 -0.117 a N = number of samples, A = total number of alleles, P % = percentage of polymorphic loci, Ar = mean allelic richness per locus, H O = observed heterozygosity, H E = expected heterozygosity, F IS = inbreeding coefficient, CI95% = confidence interval of 95%. Discussion For the first time, SNP markers obtained by the GBS method have been used to assess the genetic diversity and population structure of S. tuberosa . This study found variable levels of genetic structure among the sampled locations of umbu tree. When comparing the location of Espinosa-MG with the other locations, the F ST values were all above 0.20, varying from 0.202 to 0.330, which are considered high to very high according to Hartl and Clark (2007). And except for the four geographically closer locations from Paraíba all the other locations showed moderate genetic structure among each other, varying from 0.05 to 0.157. This result refutes the first hypothesis of this study, which expected low genetic structure among locations, considering that S. tuberosa presents allogamy and gametophytic self-incompatibility, which would favor gene flow among the studied locations. The variable levels of genetic structure observed in this study may be related to the edaphoclimatic differences among sampled locations due to the vast extension of the Caatinga biome. According to Balbino et al. ( 2018 ), the Caatinga area has approximately 850,000 km 2 , is characterized by discontinuous ecoregions, which consist of different types of vegetation, average annual temperatures that vary between 27°C and 29°C and precipitation ranging from 300 mm to 800 mm; it also comprises large plateaus up to 1,000 m and lowland peneplains. The lower levels of genetic structure among the four locations in Paraíba may be due to their smaller geographic distances than the other locations. Such geographic proximity always results in higher edaphoclimatic uniformity and provides a larger exchange of fruits through trade and by relations among the community of local people, favoring gene flow. Furthermore, the umbu fruits serve as food for many Caatinga animals, both domestic and wild animals. Therefore, it may have provided more gene flow among the closer locations such as Queimadas, Boqueirão, and Cabaceiras in the Paraíba State. Mantel’s test results also suggested isolation by distance when considering all the sampled locations but not when considering only the closest locations from the Paraíba State, in accordance with the observed patterns of genetic structure. These differences might also be explained by climatic differences in the area covered. Table 1 shows similar average annual temperatures among all collection sites, with greater variation in relation to precipitation and altitude. Espinosa-MG and Senhor do Bonfim-BA occur in As climate (tropical savannah: warm, with winter and autumn rains), with higher annual precipitation values (798.3 mm and 679.7, respectively) and altitude above 490 m, while Lagoa Grande-PE has a BSh climate (semi-arid: hot and dry, with winter rains), with precipitation of 525.3 mm and altitude of 429.9 m. It is important to note that although the three samples from Espinosa-MG certainly do not represent the genetic diversity of umbu populations from the state of Minas Gerais, these were more isolated and genetically distant. An interesting observation is that Espinosa-MG is located at a transition among the Caatinga, Atlantic Forest and Cerrado biomes (Fig. 2 ), which could also explain its high differentiation from the other locations, implying an adaptation to different ecological conditions, such as soil type, temperatures, etc. In relation to Paraíba, the Queimadas location, with an As climate, presents much higher precipitation (639 mm) than the other three locations, all with a BSh climate, including São Vicente do Seridó. This latter location is situated at a higher altitude (573.8 m) when compared to the PB locations, which might explain its slight differentiation. Other studies (Santos et al. 2008; Lins Neto et al. 2013; Balbino et al. 2018 ; Santos et al. 2021b) also observed high structuring between umbu locations, using different markers with lower genomic coverage. Santos et al. (2008) studied the genetic variation in 15 ecoregions of the Brazilian semi-arid region using AFLP markers. They observed that the genetic diversity of umbu trees was not uniformly dispersed and was highly structured between ecoregions (31.38%), suggesting restricted gene flow between populations. Balbino et al. ( 2018 ) studied the phylogeographic pattern of umbu trees using chloroplast sequences and six nuclear SSR markers in individuals from 20 locations in the states of Alagoas and Minas Gerais. They observed moderate genetic structure (13% of variation among populations) with SSR markers and described two genetic groups: a larger one containing most of the Caatinga populations and a small group closer to the Atlantic Forest, identifying the Caatinga as a large and continuous refuge and the region close to the interface between the Caatinga and the Atlantic Forest as a second refuge. Analyzing populations from Minas Gerais, Bahia, and Pernambuco, like our study, Santos et al. (2021b), based on nuclear SSR markers, observed that accessions from Bahia and Pernambuco formed a separate group from accessions from Minas Gerais, with a genetic structure equivalent to 12% among groups. In our study, Minas Gerais, Bahia, and Pernambuco locations were separated into three distinct groups, showing high genetic structure (22.1% among groups in AMOVA); they could be considered as three separate populations. This result was expected since SNP markers are more efficient in separating genetic groups than other markers (Huq et al. 2016; Leitwein et al. 2020). High genomic structure (38.6%) among locations from three Brazilian biomes based on SNP markers was reported for a fruit tree of the same genus ( S. monbim , known as “cajá”) (Silva 2021). SNP markers were used by Garcia et al. (2024) in another fruit species ( Platonia insignis , known as “bacuri”), for which even higher levels of diversity between locations were found (68.3%). The results of the present study regarding the genetic diversity of umbu locations show that the Caatinga populations of this species present moderate to high levels of diversity ( H O = 0.221 and H E = 0.199, on average), with most of the variability (77.9%) occurring within locations. This result partially refutes our second hypothesis, as despite the species being in a state of genetic vulnerability, with a reduction in its populations (Mertens et al. 2017), S. tuberosa still maintains moderate to high levels of diversity. It should be noted that these locations occupy a region of the Brazilian semi-arid with intense anthropic action; their xylopods are used to extract water during the dry period, and the plant has a reduced capacity for regeneration (Mertens et al. 2017). Still, S. tuberosa is not currently at imminent risk of extinction (Mitchell and Daly 2015; Mertens et al. 2017), although its genetic diversity may be compromised due to the destruction of its habitat. The Caatinga biome lost around 150,000 km 2 of primary vegetation between 1985 and 2020, a reduction of 26.4%, with 112,000 km 2 replaced by agriculture, and some other areas are compromised by accelerated desertification (Marques 2022). Even in this context of vulnerability, the umbu trees maintain levels of heterozygosity and diversity higher than those found for species of the same genus, such as S. mombin , which presented H O = 0.17 and H E = 0.19 on average (Silva 2021); and phylogenetically distant species, such as the fruit tree Platonia insignis ( H O = 0.081; H E = 0.092 on average) (Garcia et al. 2024). Similar genetic diversity estimates were found for other tropical trees, such as cacao ( Theobroma cacao L.) varieties in Honduras and Nicaragua ( H O = 0.206; H E = 0.367, on average) (Ji et al. 2013), Parkia platycephala Benth. located outside and inside the Sete Cidades National Park, in the state of Piauí, in a transition zone between Caatinga and Cerrado ( H O = 0.29; H E = 0.29, on average) (Morais et al. 2023). Negative inbreeding coefficients indicate an excess of heterozygotes in the studied umbu locations. These results, in addition to the high genetic diversity observed within locations, are in line with the predominantly allogamous reproductive system for the species, which also exhibits gametophytic self-incompatibility (Souza 2000; Leite and Machado 2010; Santos and Gama 2013; Santos et al. 2021). S. tuberosa is an andromonoecious species and, therefore, has equal numbers of hermaphrodite and male flowers on the same individual. Thus, the large quantity of pollen grains produced increases the fertilization viability of hermaphrodite flowers, also increasing male sexual expression (Nadia et al. 2007). Increased male sexual expression can favor cross-pollination through increased pollen flow (Symon 1979; Medan and D’Ambrogio 1998), which is enhanced by the action of pollinators. These are essential to generate new genotypic combinations and to maintain high levels of genetic variation in umbu populations. Umbu flowers have a slight sweet odor, which attracts visits from various pollinators. Nadia et al. (2007) reported 17 species of insects, including seven wasps, six bees, and four flies, as pollinators of umbu plants. Bees, Scaptotrigona postica flavisetis and Trigona fuscipennis , were the main pollinators, with emphasis also on wasps, mainly Polybia ignobilis . It is noteworthy that umbu tree has zoochoric fruits (Nadia et al. 2007) and thus has a series of characteristics, such as the presence of an edible portion involving the seed and attractive colors, which stimulate and facilitate its consumption by animals and, consequently, the dispersal of its seeds. Thus, andromonoecy, self-incompatibility, and zoochory are advantageous characteristics for maintaining the variability of umbu populations. Another important aspect to note is that local people along the Caatinga biome have an old habit as part of their culture of protecting umbu trees against fire and deforestation, especially plants that produce sweet fruits (Silva E.F., personal communication). This practice certainly provides important support for the conservation of the species; however, it results in inadvertent selection and favors recombination between plants with sweet fruits to the detriment of plants that produce acidic fruits, which are often eliminated. In conclusion, for the in situ conservation of umbu genetic resources, we can suggest all sampled sites should be considered as priority, as they have high genetic diversity and different alleles in relation to the species' gene pool. The four locations in Paraíba can be considered a single population, as they are genetically closer, although divergent individuals were also observed within them. Additionally, Minas Gerais, Pernambuco, and Bahia individuals can be considered genetically different populations. Likewise, if the interest is ex situ conservation, it is recommended to collect seeds in all locations, in each state covered by the Caatinga biome. Finally, very low levels of inbreeding were detected within the umbu locations, which seems to contradict our hypothesis of genetic vulnerability, which is a promising result for the conservation of the genetic resources of umbu tree in the Brazilian Caatinga. However, this is a promising result for the conservation of the genetic resources of the umbu tree in the Brazilian Caatinga. Declarations Funding This study was financially supported by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, 2019/04100-6). Scholarships were provided by FAPESP (2019/15544-2 to IASC and 2021/04698-9 to FMC), by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), (309445/2020-5 to EAV). Competing interests The authors declare no competing interests. Author contributions Planning and design of research: WFN, MIZ and EAV; Funding: EAV and MIZ; Material preparation, field collection expedition, and laboratory analysis: WFN, MMSGD, EFS, IASC, DPR, FRAP, CEB and CBG; Statistical analysis: AAP and FMC; The first draft of the manuscript was written by EAV and WFN; All authors commented on previous versions of the manuscript and contributed to the final manuscript. Data availability All data generated or analyzed during this study are included in this article and available in the Mendeley repository: https://data.mendeley.com/datasets/xpxs3fkdzz/1 Ethical approval This research was registered in the National System for the Management of Genetic Heritage and Associated Traditional Knowledge (SisGen) (registration nº A3AF200). It does not contain any studies with human participants or animals performed by any authors. 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(A) Group I (red) included the locations from the States of Bahia, Pernambuco, and Minas Gerais, while group II (blue) included the four locations of Paraíba State, as noticed in the B and C graphs. This analysis was performed with 71 Spondias tuberosa individuals and 5,086 SNP markers. Fig.S3.jpeg Suppl. Fig. S3 Venn diagram showing the number of discrepant SNPs (outlier loci) detected for each test (inside parentheses) and the overlap between them (numbers in ellipses) for seven sampled locations of umbu trees ( Spondias tuberosa ). 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Nascimento","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie3RsQqCQBjA8TsCXQrXk54h0MWloVdRHJrbGkpODvQVDKLHkMaTA10OWt3q6AVa27oLc+t0DLr/cp/gj/vgADCZfrJJSsNufIAtcORpDRCIFUFqKgAHLh5FwIfAbATxmhRTcd4nzhxDsjkxtMipdd/qCK8wjXiD3COF5FAyFPDQ9rmGBG0kSVYjrw3t+6xkSUBDS233nVxFTyCZHeUtl9sAaaEiu45gSdqBW1b8vRh1D0WUkmm9lkTkvo64OavEM0scB8UVme6WcrG4FjrSxYB6oO6jH7QlY34ymUymf+0FaEdYoTzly+0AAAAASUVORK5CYII=","orcid":"","institution":"Federal University of Maranhão","correspondingAuthor":true,"prefix":"","firstName":"Wellington","middleName":"Ferreira do","lastName":"Nascimento","suffix":""},{"id":291611852,"identity":"b358639b-25f5-4609-8478-fabb2503799d","order_by":1,"name":"Flaviane Malaquias Malaquias Costa","email":"","orcid":"","institution":"\"Luiz de Queiroz\" College of Agriculture, University of São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Flaviane","middleName":"Malaquias Malaquias","lastName":"Costa","suffix":""},{"id":291611853,"identity":"5c8ed20f-e86e-4d8b-a1a8-b3a2ebfc5cfd","order_by":2,"name":"Alessandro Alves-Pereira","email":"","orcid":"","institution":"Federal University of Amazonas","correspondingAuthor":false,"prefix":"","firstName":"Alessandro","middleName":"","lastName":"Alves-Pereira","suffix":""},{"id":291611854,"identity":"a2dc70dd-2499-4f88-9130-50f79c180d97","order_by":3,"name":"Carlos Eduardo de Araújo Batista","email":"","orcid":"","institution":"\"Luiz de Queiroz\" College of Agriculture, University of São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"Eduardo de Araújo","lastName":"Batista","suffix":""},{"id":291611855,"identity":"13b43521-a53f-44d5-8ffc-d972dd643187","order_by":4,"name":"Igor Araújo Santos de Carvalho","email":"","orcid":"","institution":"\"Luiz de Queiroz\" College of Agriculture, University of São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Igor","middleName":"Araújo Santos","lastName":"de Carvalho","suffix":""},{"id":291611856,"identity":"2a4398c6-ecbe-48d7-802e-750fa9d5b969","order_by":5,"name":"Caroline Bertocco Garcia","email":"","orcid":"","institution":"\"Luiz de Queiroz\" College of Agriculture, University of São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Caroline","middleName":"Bertocco","lastName":"Garcia","suffix":""},{"id":291611857,"identity":"2d07540a-072c-41e9-9406-b533e893f907","order_by":6,"name":"Allison Vieira da Silva","email":"","orcid":"","institution":"\"Luiz de Queiroz\" College of Agriculture, University of São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Allison","middleName":"Vieira da","lastName":"Silva","suffix":""},{"id":291611858,"identity":"258eff82-f214-4ac1-8125-00fe0e410333","order_by":7,"name":"Edson Ferreira da Silva","email":"","orcid":"","institution":"Federal Rural University of Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Edson","middleName":"Ferreira da","lastName":"Silva","suffix":""},{"id":291611859,"identity":"602ff6d0-9b3d-407c-a7c0-014d91772e00","order_by":8,"name":"Márcia Maria de Souza Gondim Dias","email":"","orcid":"","institution":"Federal University of Campina Grande","correspondingAuthor":false,"prefix":"","firstName":"Márcia","middleName":"Maria de Souza Gondim","lastName":"Dias","suffix":""},{"id":291611860,"identity":"9263f8f1-2c72-4623-9770-cd76896df12d","order_by":9,"name":"Fábio Rodrigo Araújo Pereira","email":"","orcid":"","institution":"Federal Institute of Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Fábio","middleName":"Rodrigo Araújo","lastName":"Pereira","suffix":""},{"id":291611861,"identity":"572f0440-dbc8-402c-add8-d14aca72b8eb","order_by":10,"name":"Maria Imaculada Zucchi","email":"","orcid":"","institution":"Paulista Agency of Agrobusiness Technology","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Imaculada","lastName":"Zucchi","suffix":""},{"id":291611862,"identity":"e96819df-8618-44b4-8e1d-2e0604efcc7f","order_by":11,"name":"Elizabeth Ann Veasey","email":"","orcid":"","institution":"\"Luiz de Queiroz\" College of Agriculture, University of São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"Ann","lastName":"Veasey","suffix":""}],"badges":[],"createdAt":"2024-04-11 16:31:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4253622/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4253622/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10722-024-02024-0","type":"published","date":"2024-05-24T00:39:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54813018,"identity":"341c6afb-076b-42b7-9a7a-fbc08cf05774","added_by":"auto","created_at":"2024-04-17 06:41:03","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1869971,"visible":true,"origin":"","legend":"\u003cp\u003eAdult umbuzeiro trees during rainy (a) and dry (b) periods. Flowers (c), fruits (d) and roots (e, f), showing the xylopod (red arrow) (f). Source: Fábio Rodrigo Araújo Pereira.\u003c/p\u003e","description":"","filename":"Fig.1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4253622/v1/06235beeee22218ce9f6d634.jpeg"},{"id":54813020,"identity":"ce5856d4-c81a-4d05-a627-eb072632d41c","added_by":"auto","created_at":"2024-04-17 06:41:04","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":527925,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Brazil with the location of collections of umbu trees (\u003cem\u003eSpondias tuberosa\u003c/em\u003e), in the states of Minas Gerais (MG), Bahia (BA), Pernambuco (PE), and Paraíba (PB).\u003c/p\u003e","description":"","filename":"Fig.2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4253622/v1/79c0b94b1e35df94f01a5cf1.jpeg"},{"id":54813021,"identity":"265176d6-eed1-4b17-9504-b82e824b7754","added_by":"auto","created_at":"2024-04-17 06:41:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1662182,"visible":true,"origin":"","legend":"\u003cp\u003eDiscriminant Analysis of Principal Components (DAPC) performed based on 5,086 SNP markers for 71 accessions of umbu tree (\u003cem\u003eSpondias tuberosa\u003c/em\u003e): a) Scatter plot of locations (collection sites) considering components 1 and 2; b) Scatter plot of locations considering components 2 and 3; c) Clustering probability analysis according to results generated in the DAPC. Each bar represents an individual, and the white lines separate the locations originating in the states of Bahia (BA), Paraíba (PB), Pernambuco (PE), and Minas Gerais (MG).\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4253622/v1/874afdbe765d97e88a4caf89.png"},{"id":54813022,"identity":"2236bd6f-b760-46ff-9aff-4ab2fb729ef4","added_by":"auto","created_at":"2024-04-17 06:41:04","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":571707,"visible":true,"origin":"","legend":"\u003cp\u003eNeighbor-joining dendrogram built with Nei’s (1978) genetic distances among 71 individuals of umbu tree (\u003cem\u003eSpondias tuberosa\u003c/em\u003e), based on 5,086 SNPs.\u003c/p\u003e","description":"","filename":"Fig.4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4253622/v1/bc906547cde7d57fb9f13e7e.jpeg"},{"id":54813023,"identity":"5c5fb4b3-3e35-4c62-a4cb-ef28361af5ce","added_by":"auto","created_at":"2024-04-17 06:41:04","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":98140,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between genetic distances (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e) and geographic distances (km) between pairs of locations from the four states (a), and between pairs of locations from Paraíba (b) performed for umbu tree (\u003cem\u003eSpondias tuberosa\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"Fig.5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4253622/v1/acd417f5eec914b34757b1b8.jpeg"},{"id":57115439,"identity":"0f65aec1-4616-4657-b0bc-0496bdf5d79d","added_by":"auto","created_at":"2024-05-25 00:39:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6281586,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4253622/v1/92dc92a3-0eea-4f95-984f-aeab402a0844.pdf"},{"id":54813604,"identity":"938d3efe-c60c-48bc-8d78-0c4c201e9dfd","added_by":"auto","created_at":"2024-04-17 06:49:03","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":102441,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSuppl. Fig. S1 \u003c/strong\u003eK-means result for detection of umbu tree (\u003cem\u003eSpondias tuberosa\u003c/em\u003e) groups, indicating k=2 groups. Analyzes were performed based on 5,086 neutral SNPmarkers.\u003c/p\u003e","description":"","filename":"Fig.S1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4253622/v1/b322e031ba6c06cdd235a6e6.jpeg"},{"id":54813025,"identity":"7856d517-8466-4cf3-9695-9337b6cfe048","added_by":"auto","created_at":"2024-04-17 06:41:04","extension":"jpeg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":279717,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSuppl. Fig. S2\u003c/strong\u003e Discriminant Analysis of Principal Component (DAPC) calculated from the k-means method. (A) Group I (red) included the locations from the States of Bahia, Pernambuco, and Minas Gerais, while group II (blue) included the four locations of Paraíba State, as noticed in the B and C graphs. This analysis was performed with 71 \u003cem\u003eSpondias tuberosa\u003c/em\u003e individuals and 5,086 SNP markers.\u003c/p\u003e","description":"","filename":"Fig.S2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4253622/v1/3e7a238938c9410206670bfe.jpeg"},{"id":54813026,"identity":"d36e43e5-2226-4289-9cf0-529db0e2b991","added_by":"auto","created_at":"2024-04-17 06:41:04","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":169279,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSuppl. Fig. S3\u003c/strong\u003e Venn diagram showing the number of discrepant SNPs (outlier loci) detected for each test (inside parentheses) and the overlap between them (numbers in ellipses) for seven sampled locations of umbu trees (\u003cem\u003eSpondias tuberosa\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"Fig.S3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4253622/v1/75ee92b277e189184b70fbcb.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"SNP-based analysis reveals high genetic structure and diversity in umbu tree (Spondias tuberosa Arruda), a native and endemic species of the Caatinga biome","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHuman activities have impacted all ecosystems on our planet, reducing their biodiversity and, consequently, their ability to maintain ecological functions and provide benefits to society (Haddad et al. 2015; Newbold et al. 2015; Miraldo et al. 2016). The Brazilian seasonally dry tropical forest called Caatinga is one of the most threatened biomes in the country due to the poorly planned use of its resources, especially concerning the removal of native vegetation (Santana and Souto 2006). Caatinga vegetation includes several endemic species, such as \u003cem\u003eSpondias tuberosa\u003c/em\u003e Arruda (Anacardiaceae), popularly known as umbu tree (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) (Souza 2000; Lins Neto et al. 2010, 2013; Mitchell and Daly 2015). Its popular name is derived from the Tupi-Guarani indigenous word \u0026ldquo;\u003cem\u003eymb-u\u003c/em\u003e\u0026rdquo;, which means \u0026ldquo;the tree that gives water\u0026rdquo; (Epstein 1998). This results from a physiological adaptation of the plant forming roots with xylopods (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef) capable of accumulating water, minerals, and organic solutes (Epstein 1998; Cavalcanti et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), which allows their survival in the dry season (Silva et al. 2008; Cavalcanti et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUmbu is an incipiently domesticated deciduous fruit tree (Lins Neto et al. 2013, 2014). Although it is native to the Caatinga, it occurs frequently in areas near the Atlantic Forest (Balbino et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), from the north of Minas Gerais State in the Southeast region to the most northern point of the Northeastern region in Brazil (Santos 1997). In this semi-arid region, this fruit tree represents an important food and medicinal resource for local residents, in addition to having high market potential (Albuquerque et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Siqueira et al. 2016; Mertens et al. 2017; Cordeiro et al. 2018). The exploitation of its fruits (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed) is mainly based on extractivism (Mertens et al. 2017), directly proportional to the flavor (if bittersweet), size, and quantity of pulp (Lins Neto et al. 2010), as they are commercially exploited for \u0026ldquo;\u003cem\u003ein natura\u003c/em\u003e\u0026rdquo; consumption and preparation of juices, jellies, ice creams, sweets, and frozen pulp (Mertens et al. 2017). The fruits and leaves are also used as fodder for small domestic mammals such as sheep and goats (Cavalcanti et al. 2004). In traditional medicine, different parts of the plant have been used to treat venereal diseases, digestive disorders, diarrhea, diabetes, menstrual disturbances, and placental delivery (Albuquerque et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Siqueira et al. 2016; Cordeiro et al. 2018). Siqueira et al. (2016) reported evidence of an anti-inflammatory action using the leaves, suggesting potential therapeutic benefits for inflammatory conditions. The pharmacological potential of the leaf extract as an antioxidant and antifungal agent was also demonstrated by Cordeiro et al. (2018).\u003c/p\u003e \u003cp\u003e \u003cem\u003eS. tuberosa\u003c/em\u003e is an andromonoecious species with gametophytic self-incompatibility (Leite and Machado 2010), pollinated mainly by bees and wasps (Nadia et al. 2007; Almeida et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). It is a predominantly allogamous species, with an estimated outcrossing rate of 74% (Souza 2000), and variation between 80.4% (multi-locus) and 84.1% (single locus) (Santos and Gama 2013). However, fruit production is low, considering the high number of flowers produced (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). According to Stephenson (1981), this may be related to extrinsic factors, such as limiting environmental resources, and it may also be related to intrinsic factors, such as zoochoric fruits of high energy value aborted at a young age (Nadia et al. 2007).\u003c/p\u003e \u003cp\u003eIn the Caatinga, the exploration of extensive pastures is the predominant anthropic disturbance (Alves et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and combined with the destruction of their habitat (Mertens et al. 2017; Balbino et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), induces a reduction of the umbu tree populations, which may compromise the genetic diversity of the species (Mitchell and Daly 2015). It is supposed that much of the existing genetic variability of \u003cem\u003eS. tuberosa\u003c/em\u003e has been lost due to indiscriminate deforestation, rapid advance of agricultural frontiers for the plantation of exotic crops, and urban expansion in their respective areas of occurrence, which may have been enhanced by natural threats, such as climate change (Mertens et al. 2017). Therefore, understanding the species' genetic diversity is essential to rationalize the use of its genetic resources, elaborate efficient conservation strategies, and develop genetic improvement programs (Souza et al. 2016).\u003c/p\u003e \u003cp\u003eSeveral molecular markers have been used to assess the genetic diversity of this species, such as RAPD (Random Amplified Polymorphic DNA) (Moreira et al. 2007), AFLP (Amplified Fragment Length Polymorphism) (Santos et al. 2008; Santos and Oliveira 2008; Santos et al. 2011), ISSR (Inter-simple Sequence Repeat) (Lins Neto et al. 2013), and SSR (Simple Sequence Repeat) (Balbino et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Santos et al. 2021a,b). Only one study was found using SNP (Single Nucleotide Polymorphisms) markers in \u003cem\u003eS. tuberosa\u003c/em\u003e (Nobre et al. 2018) addressing the hybrid origin of \u003cem\u003eS. bahiensis\u003c/em\u003e P. Carvalho, van Den Berg \u0026amp; M. Machado.\u003c/p\u003e \u003cp\u003eThe genotyping-by-sequencing (GBS) technique is based on the complexity reduction of genomic DNA by restriction enzymes and on the use of barcode DNA adapters to produce multiplexed libraries of samples that are submitted to next-generation sequencing (NGS) (Poland and Rife 2012). With this combination, the technique has demonstrated the ability to produce thousands of SNPs in several species, including fruit trees (Goonetilleke et al. 2018). SNP markers are the most abundant genetic polymorphisms in the genome. In addition to the evaluation of neutral variation, they enable the study and identification of regions of the genome that might be under natural selection in the population (outlier loci), that is, regions possibly associated with adaptation (Luikart et al. 2003; Cortinovis et al. 2020; Alves-Pereira et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)⁠. The present study aimed to assess the genomic diversity and structure of umbu trees (\u003cem\u003eS. tuberosa\u003c/em\u003e) from seven locations in the Caatinga biome by SNP markers obtained through the GBS technique. Two complimentary hypotheses were tested: a) because \u003cem\u003eS. tuberosa\u003c/em\u003e is an endemic species of the Caatinga, predominantly allogamous, presenting gametophytic self-incompatibility, we expected to find low genetic structure between locations; b) the species is in a state of genetic vulnerability, highly threatened by anthropogenic activities, showing a reduction in its populations and, consequently, in its genetic diversity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSampling, DNA extraction, and quantification\u003c/h2\u003e \u003cp\u003eIn the Caatinga biome, the umbu tree occurs in areas of native vegetation but mainly in anthropized ones such as those currently cultivated, pasturelands, homegardens and areas of regeneration of native vegetation after being abandoned for agricultural use. In these areas, young leaves were sampled from a total of 71 umbu trees in seven locations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e): in Minas Gerais State (MG), between the municipalities of Espinosa and Monte Azul, named Espinosa in this study, in cultivated areas and areas of regeneration of native vegetation; in Bahia State (BA), between the municipalities of Jaguarari and Senhor do Bonfim, named as Senhor do Bonfim in this study, in anthropized remnant fragments; in Pernambuco State (PE), between the municipalities of Lagoa Grande and Santa Maria da Boa Vista, named as Lagoa Grande in this study, in cultivated areas and in areas of regeneration of native vegetation; and four areas in Paraiba State, in the municipalities of S\u0026atilde;o Vicente do Serid\u0026oacute; and Queimadas, in homegardens and agriculture cultivated areas; Boqueir\u0026atilde;o, only in agriculture cultivated areas; and Cabaceiras, in areas of natural vegetation and homegardens, with cattle and goat farming). It was established that the minimum distance among sampled individuals was 500 m, and the maximum was 3,000 m.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSampling locations of umbu trees (\u003cem\u003eSpondias tuberosa\u003c/em\u003e), from seven locations distributed in the states of Minas Gerais (MG), Bahia (BA), Para\u0026iacute;ba (PB), and Pernambuco (PE), in the Caatinga biome, including number of individuals (N) sampled, geographic coordinates and climate data (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://koppenbrasil.github.io/\u003c/span\u003e\u003cspan address=\"https://koppenbrasil.github.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Annual Temp.\u003c/p\u003e \u003cp\u003e(\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003cp\u003eRain\u003c/p\u003e \u003cp\u003e(mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAltitude\u003c/p\u003e \u003cp\u003e(m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKoppen\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEspinosa-MG\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u0026deg;05\u0026rsquo;27.5\u0026rdquo;S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u0026deg;47\u0026rsquo;49.0\u0026rdquo;W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e798.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e675.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenhor do Bonfim-BA\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u0026deg;18\u0026rsquo;23.0\u0026rdquo;S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u0026deg;09\u0026rsquo;44.0\u0026rdquo;W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e679.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e498.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u0026atilde;o Vicente do Serid\u0026oacute;-PB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e06\u0026deg;53\u0026rsquo;40.2\u0026rdquo;S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36\u0026deg;24\u0026rsquo;12.3\u0026rdquo;W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e455.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e573.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBSh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQueimadas-PB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e07\u0026deg;26\u0026rsquo;08.4\u0026rdquo;S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u0026deg;53\u0026rsquo;12.4\u0026rdquo;W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e639.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e407.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoqueir\u0026atilde;o-PB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e07\u0026deg;27\u0026rsquo;19.2\u0026rdquo;S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36\u0026deg;06\u0026rsquo;1.5\u0026rdquo;W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e466.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e424.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBSh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCabaceiras-PB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e07\u0026deg;13\u0026rsquo;22.3\u0026rdquo;S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u0026deg;53\u0026rsquo;41.3\u0026rdquo;W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e429.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e444.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBSh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLagoa Grande-PE\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e08\u0026deg;57\u0026rsquo;27.0\u0026rdquo;S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u0026deg;11\u0026rsquo;40.0\u0026rdquo;W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e525.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e429.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBSh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003eKoppen classification: As (Tropical savannah: warm, with winter and autumn rains); BSh (Semi-arid: hot and dry, with winter rains).\u003c/p\u003e \u003cp\u003eThe extraction of genomic DNA was performed using the protocol described by Inglis et al. (2018) with modifications, including three to four prewashes with sorbitol buffer [100 mM Tris-HCl pH 8.0, 0.35 M Sorbitol, 5 mM EDTA pH 8.0, 1% (w/v) Polyvinylpyrrolidone (average molecular weight 40,000; PVP-40)]. The quantification and analysis of DNA quality were performed through electrophoresis in a 1% agarose gel (w/v) stained with Gel Red (Biotium). DNA was quantified based on the phage λ molecular size standards (Invitrogen) at different concentrations (20, 50, and 100 ng \u0026micro;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and validated with a Qubit4 fluorometer (Invitrogen). After quantification, DNA samples were normalized to a 20 ng/uL concentration for GBS library preparation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGenomic library and SNPs identification\u003c/h2\u003e \u003cp\u003eThe genomic library was prepared following the protocol described by Poland et al. (2012). Briefly describing, high-quality genomic DNA (140 ng per sample) was digested at 37\u0026deg;C for 12 hours using a combination of a \u003cem\u003ePstI\u003c/em\u003e rare-cutting enzyme (NEB-New England Biolabs) with a \u003cem\u003eMseI\u003c/em\u003e frequent-cutting enzyme (NEB-New England Biolabs). The fragments generated from the digestion for each sample were ligated to adapters containing specific barcode sequences using the enzyme DNA T4 ligase and grouped together in a 96-plex. The multiplex was column purified and amplified for PCR enrichment, and it is being submitted to a new purification step. The library was then qualitatively evaluated using the BioAnalyzer system (Agilent Technologies) and quantified using the NEBNext\u0026reg; Library Quant Kit for Illumina (New England Biolabs) on the CFX 384 Touch Real Time PCR Detection System (Bio-Rad Laboratories). Subsequently, the library was sequenced in a flowcell using a sequencer on the HiSeq2500 Illumina platform, with the company EcoMol, at the Genomics Center of ESALQ/USP.\u003c/p\u003e \u003cp\u003eThe overall sequencing quality was assessed with FastQC (Andrews \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and the removal of low-quality sequences containing adapters and trimming of sequences to 80 bases was performed with Trimmomatic 0.39 (Bolger et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). \u003cem\u003eDe novo\u003c/em\u003e identification of SNP markers was performed with Stacks-1.42 (Catchen et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Sequence demultiplexing for each sample and checking the integrity of restriction sites was performed with the process_radtags module. The initial assembly of loci for each sample was performed with the ustacks module with the parameters of minimum sequencing depth (-m) of 3x and maximum distance allowed between sequences of the same locus (-M) of 2 bases. A catalogue of loci was obtained with the cstacks program allowing a maximum distance between loci of different samples (-n) of 2 bases. The sstacks module was used to compare the loci of each sample with the catalogue loci, and the rxstacks module was used to remove the loci with a lower probability (--lnl_lim \u0026minus;\u0026thinsp;10). The populations module was used for final data filtering, considering SNP markers as the loci with a minimum sequencing depth of 5x, frequency of the rarest allele (MAF)\u0026thinsp;\u0026ge;\u0026thinsp;0.01, presence of the SNP in at least 90% of the samples of each one of the sampled locations, and retaining only one SNP per sequenced tag. Sequencing quality metrics of SNP markers were obtained with VCFtools 0.1.17 (Danecek et al. 2011).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eIdentification of possible outlier loci\u003c/h2\u003e \u003cp\u003eThe identification of possible outlier loci was performed considering the sampled locations. Three complementary tests were performed: Pcadapt (Luu et al. 2017) in which the outlier loci are associated with the genetic groups observed in a principal component analysis (PCA); FstHet (Flanagan and Jones 2017) for identifying loci with excessive high or low F\u003csub\u003eST\u003c/sub\u003e values ​​in relation to a neutral distribution, and BayeScan (Foll and Gaggiotti 2008), a Bayesian analysis for estimating posterior probabilities to verify whether or not each locus reflects selection. The pcadapt analysis was performed considering the first four principal components, which suggested great agreement between genetic groups and sample locations. In this analysis, SNP markers with q-values ​​\u0026lt; 0.1 were considered as outliers. The fstHet analysis was performed based on the beta hat estimate (Cockerham and Weir 1993) (analogous to Wrigth\u0026rsquo;s F\u003csub\u003eST\u003c/sub\u003e), considering as outliers the SNP markers above or below a 95% confidence interval constructed based on 1000 bootstraps. The above analyses were performed with the R packages (R Core Team 2018), pcadapt (Luu et al. 2017) and fsthet (Flanagan and Jones 2017). BayeScan 2.1 (Foll and Gaggiotti 2008) was used to perform 20 pilot runs, with 100000 iterations each, followed by 250000 burn-in steps and 25000 steps with intervals of 50 (total of 1500000 iterations). It was considered in the model that the probability of including selection was 3x lower than that of not including selection. In this analysis, SNP markers with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered outliers.\u003c/p\u003e \u003cp\u003eFalse positives are frequent in the detection of outlier loci (Luikart et al. 2003). For this reason, the final set of outlier markers consisted of the loci identified in at least two of the three applied tests, as suggested by Luikart et al. (2003) and considered by Alves-Pereira et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses carried out with neutral SNP loci\u003c/h2\u003e \u003cp\u003eThe genetic structure and diversity analyses were performed with neutral SNPs, excluding the outlier loci according to the criterion described above. Discriminant analysis of principal components (DAPC) was performed with the adegenet package (Jombart 2008) in the R program (R Development Core Team 2018). The number of clusters from the DAPC was calculated by the K-means method, which runs different probabilities of cluster numbers. By using this method, two groups were detected whereas group I included the locations from the States of Bahia, Pernambuco, and Minas Gerais, while group II included the four locations of Para\u0026iacute;ba State (Figs. S1 and S2). The DAPC was also performed using the locations as a \u003cem\u003epriori\u003c/em\u003e groupings. The K-means method retained only one principal component that explained 12.4% of the total variation. In the DAPC analysis based on locations, six principal components were retained, of which the first three explained 26.7% of the variation. Therefore, the analyses were continued based on the locations, as they summarized a greater percentage of the genetic variation.\u003c/p\u003e \u003cp\u003eThe genetic relationship between the samples was performed by cluster analysis with the neighbor-joining method, using Nei\u0026rsquo;s genetic distances (Nei 1978) performed with the ape package (Paradis and Schliep 2019) in the R program (R Development Core Team 2018). The dendrogram was edited with FigTree v.1.4.3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tree.bio.ed.ac.uk/softwere/figtree/\u003c/span\u003e\u003cspan address=\"http://tree.bio.ed.ac.uk/softwere/figtree/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Pairwise F\u003csub\u003eST\u003c/sub\u003e matrices among locations and among the groups delimited by DAPC were calculated with the poppr package (Kamvar et al. 2014) in the R program (R Development Core Team 2018).\u003c/p\u003e \u003cp\u003eThe genetic diversity parameters for the sampled locations of the total number of alleles (\u003cem\u003eA\u003c/em\u003e), observed heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eO\u003c/em\u003e\u003c/sub\u003e), and expected heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eE\u003c/em\u003e\u003c/sub\u003e), in addition to the inbreeding coefficient (\u003cem\u003ef\u003c/em\u003e), were estimated using the hierfstat (Goudet and Jombart 2020) and poppr (Kamvar et al. 2014) packages in the R program (R Development Core Team 2018). The distribution of genetic variability between and within locations was detected using the analysis of molecular variance (AMOVA) with the hierfstat (Goudet and Jombart 2020) and poppr (Kamvar et al. 2014) packages in the R program (R Development Core Team 2018). To verify the existence of isolation by distance, the Mantel test was performed with the ade4 package (Dray and Dufour 2007; Dray et al. 2007; Bougeard and Dray \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Thioulouse et al. 2018), aiming to evaluate the correlation between the genetic divergence from the F\u003csub\u003eST\u003c/sub\u003e values ​​of the pairwise matrix between locations and the geographic distances (km), generated from the geographic coordinates, obtained with the geodist package (Padgham and Sumner 2020). A second Mantel test was performed with only the four populations of Para\u0026iacute;ba, geographically located closer to each other. The significance level was considered based on 20000 permutations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSNP detection and outlier SNPs loci\u003c/h2\u003e \u003cp\u003eA total of 5,336 SNP markers were identified for 71 samples (mean sequencing depth\u0026thinsp;=\u0026thinsp;55.4x; standard deviation\u0026thinsp;=\u0026thinsp;33.3; mean of 0.54% missing data). Of these, 1,624 SNPs were identified as outlier markers (1,029 by the pcadapt program, 468 by the fsthet program, and 127 by the BayeScan program) (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). The final set of outlier markers consisted of 250 SNPs identified by at least two of the three tests performed (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Therefore, the analyses of genetic diversity and population structure were performed with 5,086 SNPs considered neutral, excluding from the total the 250 SNPs identified as outlier loci.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenomic structure among umbu tree locations\u003c/h3\u003e\n\u003cp\u003eSix main components were retained for the DAPC based on locations, of which the first three explained 26.7% of the variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). A strong structure was found among the umbu tree samples from different locations, especially among the sampled states (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), establishing an optimal number of groups corresponding to four groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The locations of Espinosa-MG, Senhor do Bonfim-BA, and Lagoa Grande-PE formed three isolated groups, all genetically different from each other. As for the four populations collected in Para\u0026iacute;ba, all geographically close to each other (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), there was an overlapping of genotypes suggesting greater genetic similarity among them.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe neighbor-joining dendrogram based on Nei's genetic distances (1978) clustered the samples into four groups: group I, consisting of individuals from Lagoa Grande-PE; group II, consisting of individuals from Senhor do Bonfim-BA; group III, consisting of individuals from Espinosa-MG; and group IV grouping the individuals from the four locations sampled in Para\u0026iacute;ba, with the individuals from S\u0026atilde;o Vicente do Serid\u0026oacute;-PB being the most divergent in relation to the individuals from the other locations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Mantel test showed a high and significant correlation between genetic distances (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e) and geographic distances (km) (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.974; p\u0026thinsp;=\u0026thinsp;0.0015) between pairs of locations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). However, when the Mantel test was conducted with only the four locations from Para\u0026iacute;ba (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eb) the result was non-significant (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.575; p\u0026thinsp;=\u0026thinsp;0.212), which was already expected due to their greater geographical and genetic proximity (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e estimates (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), Espinosa-MG is genetically the most distinct from the other locations, with \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e values ​​ranging from 0.202 (Espinosa-MG and Lagoa Grande-PE) to 0.330 (Espinosa-MG and Boqueir\u0026atilde;o-PB). The locations of Senhor do Bonfim-BA and Lagoa Grande-PE are the next more genetically distant from the others. However, both are genetically closer to each other. The locations in Para\u0026iacute;ba are genetically closer to each other, with the \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e values ​​ranging from 0.028 (Boqueir\u0026atilde;o and Cabaceiras) to 0.074 (S\u0026atilde;o Vicente and Queimadas).\u003c/p\u003e \u003cp\u003eThe AMOVA showed that most of the observed genetic variation was found within locations (77.9%) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, the variation among locations (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e = 0.221) is high (Hartl and Clark 2007) and significant, corroborating the results observed in the DAPC and in the dendrogram (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e estimates between pairs of locations (lower diagonal) and respective 95% confidence intervals (upper diagonal) for the umbu trees (\u003cem\u003eSpondias tuberosa\u003c/em\u003e) locations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenhor do Bonfim-BA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u0026atilde;o Vicente-PB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQueimadas-PB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBoqueir\u0026atilde;o-PB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCabaceiras-PB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLagoa Grande-PE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEspinosa-MG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSenhor do Bonfim-BA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.113:0.131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.149:0.164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(0.144:0.160)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.143:0.156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.047:0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e(0.198:0.218)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS\u0026atilde;o Vicente-PB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.065:0.083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(0.049:0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.032:0.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.091:0.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e(0.283:0.308)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQueimadas-PB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(0.051:0.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.041:0.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.127:0.140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e(0.315:0.340)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBoqueir\u0026atilde;o-PB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.024:0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.121:0.135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e(0.318:0.342)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCabaceiras-PB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.115:0.127)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e(0.310:0.334)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLagoa Grande-PE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e(0.193:0.211)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEspinosa-MG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of molecular variance (AMOVA) based on 5,086 SNPs used to identify sources of genetic variability between and within sampled locations of umbu trees (\u003cem\u003eSpondias tuberosa\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDF\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePhiST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmong locations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12822.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2137.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin locations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37242.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e581.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50065.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e715.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003eDF = degrees of freedom, SS\u0026thinsp;=\u0026thinsp;sum of squares, MS\u0026thinsp;=\u0026thinsp;mean squares, PhiST\u0026thinsp;=\u0026thinsp;estimate analogous to \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenetic diversity for the umbu tree locations\u003c/h2\u003e \u003cp\u003eIn the analysis of genetic diversity for each location (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the total number of alleles ranged from 7,231 (S\u0026atilde;o Vicente-PB) to 9,402 (Lagoa Grande-PE), with an average of 8,136.7 alleles. All locations presented polymorphism greater than 71%, and allelic richness ranged from 1.352 (S\u0026atilde;o Vicente-PB) to 1.538 (Lagoa Grande-PE). Results show moderate to high levels of genetic diversity for the species, with an excess of heterozygotes in all locations except for Lagoa Grande-PE. This can be evidenced by the negative and close to zero inbreeding coefficients (mean \u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e = -0.117). The locations of Espinosa-MG, Lagoa Grande-PE and Senhor do Bonfim-BA stand out as having the highest heterozygosities, while the four locations in Para\u0026iacute;ba (PB) State showed the lowest heterozygosities.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenetic diversity parameters and inbreeding coefficient for umbu trees (\u003cem\u003eSpondias tuberosa\u003c/em\u003e) locations evaluated with 5,086 SNPs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eA\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e(\u003cem\u003e%\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAr\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eO\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eE\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eIS\u003c/em\u003e\u003c/sub\u003e CI95%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEspinosa-MG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.787:-0.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenhor do Bonfim-BA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.029: 0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u0026atilde;o Vicente-PB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.801:-0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQueimadas-PB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.203:-0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoqueir\u0026atilde;o-PB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.287:-0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCabaceiras-PB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c9\"\u003e \u003cp\u003e-0.084:-0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLagoa Grande-PE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.017: 0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,136.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003eN = number of samples, \u003cem\u003eA\u003c/em\u003e\u0026thinsp;=\u0026thinsp;total number of alleles, \u003cem\u003eP\u003c/em\u003e% = percentage of polymorphic loci, \u003cem\u003eAr\u003c/em\u003e\u0026thinsp;=\u0026thinsp;mean allelic richness per locus, \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eO\u003c/em\u003e\u003c/sub\u003e = observed heterozygosity, \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eE\u003c/em\u003e\u003c/sub\u003e = expected heterozygosity, \u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e = inbreeding coefficient, CI95% = confidence interval of 95%.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eFor the first time, SNP markers obtained by the GBS method have been used to assess the genetic diversity and population structure of \u003cem\u003eS. tuberosa\u003c/em\u003e. This study found variable levels of genetic structure among the sampled locations of umbu tree. When comparing the location of Espinosa-MG with the other locations, the \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e values were all above 0.20, varying from 0.202 to 0.330, which are considered high to very high according to Hartl and Clark (2007). And except for the four geographically closer locations from Para\u0026iacute;ba all the other locations showed moderate genetic structure among each other, varying from 0.05 to 0.157. This result refutes the first hypothesis of this study, which expected low genetic structure among locations, considering that \u003cem\u003eS. tuberosa\u003c/em\u003e presents allogamy and gametophytic self-incompatibility, which would favor gene flow among the studied locations. The variable levels of genetic structure observed in this study may be related to the edaphoclimatic differences among sampled locations due to the vast extension of the Caatinga biome. According to Balbino et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the Caatinga area has approximately 850,000 km\u003csup\u003e2\u003c/sup\u003e, is characterized by discontinuous ecoregions, which consist of different types of vegetation, average annual temperatures that vary between 27\u0026deg;C and 29\u0026deg;C and precipitation ranging from 300 mm to 800 mm; it also comprises large plateaus up to 1,000 m and lowland peneplains.\u003c/p\u003e \u003cp\u003eThe lower levels of genetic structure among the four locations in Para\u0026iacute;ba may be due to their smaller geographic distances than the other locations. Such geographic proximity always results in higher edaphoclimatic uniformity and provides a larger exchange of fruits through trade and by relations among the community of local people, favoring gene flow. Furthermore, the umbu fruits serve as food for many Caatinga animals, both domestic and wild animals. Therefore, it may have provided more gene flow among the closer locations such as Queimadas, Boqueir\u0026atilde;o, and Cabaceiras in the Para\u0026iacute;ba State.\u003c/p\u003e \u003cp\u003eMantel\u0026rsquo;s test results also suggested isolation by distance when considering all the sampled locations but not when considering only the closest locations from the Para\u0026iacute;ba State, in accordance with the observed patterns of genetic structure. These differences might also be explained by climatic differences in the area covered. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows similar average annual temperatures among all collection sites, with greater variation in relation to precipitation and altitude. Espinosa-MG and Senhor do Bonfim-BA occur in As climate (tropical savannah: warm, with winter and autumn rains), with higher annual precipitation values (798.3 mm and 679.7, respectively) and altitude above 490 m, while Lagoa Grande-PE has a BSh climate (semi-arid: hot and dry, with winter rains), with precipitation of 525.3 mm and altitude of 429.9 m. It is important to note that although the three samples from Espinosa-MG certainly do not represent the genetic diversity of umbu populations from the state of Minas Gerais, these were more isolated and genetically distant. An interesting observation is that Espinosa-MG is located at a transition among the Caatinga, Atlantic Forest and Cerrado biomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which could also explain its high differentiation from the other locations, implying an adaptation to different ecological conditions, such as soil type, temperatures, etc. In relation to Para\u0026iacute;ba, the Queimadas location, with an As climate, presents much higher precipitation (639 mm) than the other three locations, all with a BSh climate, including S\u0026atilde;o Vicente do Serid\u0026oacute;. This latter location is situated at a higher altitude (573.8 m) when compared to the PB locations, which might explain its slight differentiation.\u003c/p\u003e \u003cp\u003eOther studies (Santos et al. 2008; Lins Neto et al. 2013; Balbino et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Santos et al. 2021b) also observed high structuring between umbu locations, using different markers with lower genomic coverage. Santos et al. (2008) studied the genetic variation in 15 ecoregions of the Brazilian semi-arid region using AFLP markers. They observed that the genetic diversity of umbu trees was not uniformly dispersed and was highly structured between ecoregions (31.38%), suggesting restricted gene flow between populations. Balbino et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) studied the phylogeographic pattern of umbu trees using chloroplast sequences and six nuclear SSR markers in individuals from 20 locations in the states of Alagoas and Minas Gerais. They observed moderate genetic structure (13% of variation among populations) with SSR markers and described two genetic groups: a larger one containing most of the Caatinga populations and a small group closer to the Atlantic Forest, identifying the Caatinga as a large and continuous refuge and the region close to the interface between the Caatinga and the Atlantic Forest as a second refuge. Analyzing populations from Minas Gerais, Bahia, and Pernambuco, like our study, Santos et al. (2021b), based on nuclear SSR markers, observed that accessions from Bahia and Pernambuco formed a separate group from accessions from Minas Gerais, with a genetic structure equivalent to 12% among groups. In our study, Minas Gerais, Bahia, and Pernambuco locations were separated into three distinct groups, showing high genetic structure (22.1% among groups in AMOVA); they could be considered as three separate populations. This result was expected since SNP markers are more efficient in separating genetic groups than other markers (Huq et al. 2016; Leitwein et al. 2020). High genomic structure (38.6%) among locations from three Brazilian biomes based on SNP markers was reported for a fruit tree of the same genus (\u003cem\u003eS. monbim\u003c/em\u003e, known as \u0026ldquo;caj\u0026aacute;\u0026rdquo;) (Silva 2021). SNP markers were used by Garcia et al. (2024) in another fruit species (\u003cem\u003ePlatonia insignis\u003c/em\u003e, known as \u0026ldquo;bacuri\u0026rdquo;), for which even higher levels of diversity between locations were found (68.3%).\u003c/p\u003e \u003cp\u003eThe results of the present study regarding the genetic diversity of umbu locations show that the Caatinga populations of this species present moderate to high levels of diversity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eO\u003c/em\u003e\u003c/sub\u003e = 0.221 and \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eE\u003c/em\u003e\u003c/sub\u003e = 0.199, on average), with most of the variability (77.9%) occurring within locations. This result partially refutes our second hypothesis, as despite the species being in a state of genetic vulnerability, with a reduction in its populations (Mertens et al. 2017), \u003cem\u003eS. tuberosa\u003c/em\u003e still maintains moderate to high levels of diversity. It should be noted that these locations occupy a region of the Brazilian semi-arid with intense anthropic action; their xylopods are used to extract water during the dry period, and the plant has a reduced capacity for regeneration (Mertens et al. 2017). Still, \u003cem\u003eS. tuberosa\u003c/em\u003e is not currently at imminent risk of extinction (Mitchell and Daly 2015; Mertens et al. 2017), although its genetic diversity may be compromised due to the destruction of its habitat. The Caatinga biome lost around 150,000 km\u003csup\u003e2\u003c/sup\u003e of primary vegetation between 1985 and 2020, a reduction of 26.4%, with 112,000 km\u003csup\u003e2\u003c/sup\u003e replaced by agriculture, and some other areas are compromised by accelerated desertification (Marques 2022).\u003c/p\u003e \u003cp\u003eEven in this context of vulnerability, the umbu trees maintain levels of heterozygosity and diversity higher than those found for species of the same genus, such as \u003cem\u003eS. mombin\u003c/em\u003e, which presented \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eO\u003c/em\u003e\u003c/sub\u003e = 0.17 and \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eE\u003c/em\u003e\u003c/sub\u003e = 0.19 on average (Silva 2021); and phylogenetically distant species, such as the fruit tree \u003cem\u003ePlatonia insignis\u003c/em\u003e (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eO\u003c/em\u003e\u003c/sub\u003e = 0.081; \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eE\u003c/em\u003e\u003c/sub\u003e = 0.092 on average) (Garcia et al. 2024). Similar genetic diversity estimates were found for other tropical trees, such as cacao (\u003cem\u003eTheobroma cacao\u003c/em\u003e L.) varieties in Honduras and Nicaragua (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eO\u003c/em\u003e\u003c/sub\u003e = 0.206; \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eE\u003c/em\u003e\u003c/sub\u003e = 0.367, on average) (Ji et al. 2013), \u003cem\u003eParkia platycephala\u003c/em\u003e Benth. located outside and inside the Sete Cidades National Park, in the state of Piau\u0026iacute;, in a transition zone between Caatinga and Cerrado (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eO\u003c/em\u003e\u003c/sub\u003e = 0.29; \u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eE\u003c/em\u003e\u003c/sub\u003e = 0.29, on average) (Morais et al. 2023).\u003c/p\u003e \u003cp\u003eNegative inbreeding coefficients indicate an excess of heterozygotes in the studied umbu locations. These results, in addition to the high genetic diversity observed within locations, are in line with the predominantly allogamous reproductive system for the species, which also exhibits gametophytic self-incompatibility (Souza 2000; Leite and Machado 2010; Santos and Gama 2013; Santos et al. 2021). \u003cem\u003eS. tuberosa\u003c/em\u003e is an andromonoecious species and, therefore, has equal numbers of hermaphrodite and male flowers on the same individual. Thus, the large quantity of pollen grains produced increases the fertilization viability of hermaphrodite flowers, also increasing male sexual expression (Nadia et al. 2007). Increased male sexual expression can favor cross-pollination through increased pollen flow (Symon 1979; Medan and D\u0026rsquo;Ambrogio 1998), which is enhanced by the action of pollinators. These are essential to generate new genotypic combinations and to maintain high levels of genetic variation in umbu populations. Umbu flowers have a slight sweet odor, which attracts visits from various pollinators. Nadia et al. (2007) reported 17 species of insects, including seven wasps, six bees, and four flies, as pollinators of umbu plants. Bees, \u003cem\u003eScaptotrigona postica flavisetis\u003c/em\u003e and \u003cem\u003eTrigona fuscipennis\u003c/em\u003e, were the main pollinators, with emphasis also on wasps, mainly \u003cem\u003ePolybia ignobilis\u003c/em\u003e. It is noteworthy that umbu tree has zoochoric fruits (Nadia et al. 2007) and thus has a series of characteristics, such as the presence of an edible portion involving the seed and attractive colors, which stimulate and facilitate its consumption by animals and, consequently, the dispersal of its seeds. Thus, andromonoecy, self-incompatibility, and zoochory are advantageous characteristics for maintaining the variability of umbu populations. Another important aspect to note is that local people along the Caatinga biome have an old habit as part of their culture of protecting umbu trees against fire and deforestation, especially plants that produce sweet fruits (Silva E.F., personal communication). This practice certainly provides important support for the conservation of the species; however, it results in inadvertent selection and favors recombination between plants with sweet fruits to the detriment of plants that produce acidic fruits, which are often eliminated.\u003c/p\u003e \u003cp\u003eIn conclusion, for the \u003cem\u003ein situ\u003c/em\u003e conservation of umbu genetic resources, we can suggest all sampled sites should be considered as priority, as they have high genetic diversity and different alleles in relation to the species' gene pool. The four locations in Para\u0026iacute;ba can be considered a single population, as they are genetically closer, although divergent individuals were also observed within them. Additionally, Minas Gerais, Pernambuco, and Bahia individuals can be considered genetically different populations. Likewise, if the interest is \u003cem\u003eex situ\u003c/em\u003e conservation, it is recommended to collect seeds in all locations, in each state covered by the Caatinga biome. Finally, very low levels of inbreeding were detected within the umbu locations, which seems to contradict our hypothesis of genetic vulnerability, which is a promising result for the conservation of the genetic resources of umbu tree in the Brazilian Caatinga. However, this is a promising result for the conservation of the genetic resources of the umbu tree in the Brazilian Caatinga.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was financially supported by \u003cem\u003eFunda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Estado de S\u0026atilde;o Paulo\u003c/em\u003e (FAPESP,\u0026nbsp;2019/04100-6). Scholarships were provided\u0026nbsp;by FAPESP (2019/15544-2\u0026nbsp;to IASC and\u0026nbsp;2021/04698-9 to FMC), by\u0026nbsp;\u003cem\u003eConselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico\u003c/em\u003e (CNPq), (309445/2020-5 to EAV).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlanning and design of research: WFN, MIZ and EAV; Funding: EAV and MIZ; Material preparation, field collection expedition, and laboratory analysis: WFN,\u0026nbsp;MMSGD,\u0026nbsp;EFS, IASC, DPR,\u0026nbsp;FRAP,\u0026nbsp;CEB and CBG; Statistical analysis: AAP and FMC; The first draft of the manuscript was written by EAV and WFN; All authors commented on previous versions of the manuscript and contributed to the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAll data generated or analyzed during this study are included in this article and available in the Mendeley\u0026nbsp;\u003c/em\u003e\u003cem\u003erepository:\u0026nbsp;\u003c/em\u003ehttps://data.mendeley.com/datasets/xpxs3fkdzz/1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was registered in the National System for the Management of Genetic Heritage and Associated Traditional Knowledge (SisGen) (registration n\u0026ordm; A3AF200). It does not contain any studies with human participants or animals performed by any authors.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlbuquerque UP, Medeiros PM, Almeida ALS, Monteiro JM, Freitas Lins Neto EM, Melo JG and Santos JP (2007) Medicinal plants of the caatinga (semi-arid) vegetation of NE Brazil: a quantitative approach. J Ethnopharmacol 3:325-354. https://doi.org/10.1016/j.jep.2007.08.017\u003c/li\u003e\n\u003cli\u003eAlmeida ALS, Albuquerque UP, Castro CC (2011) Reproductive biology of \u003cem\u003eSpondias tuberosa\u003c/em\u003e Arruda (Anacardiaceae), an endemic fructiferous species of the caatinga (dry forest), under different management conditions in northeastern Brazil. 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In:\u003cem\u003e \u003c/em\u003eHawkes JG, Lester RN, Skelding AD (ed). The biology and taxonomy of the Solanaceae, 1st. Academic Press, London, pp 385-398\u003c/li\u003e\n\u003cli\u003eThioulouse J, Dray S, Dufour A, Siberchicot A, Jombart T, Pavoine S (2018) Multivariate analysis of ecological data with ade4. Springer, New York \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Genetic diversity, population structure, Caatinga biome, native fruit, endemic species","lastPublishedDoi":"10.21203/rs.3.rs-4253622/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4253622/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUmbu (\u003cem\u003eSpondias tuberosa\u003c/em\u003e Arruda) is an endemic fruit tree restricted to the Brazilian seasonally dry tropical forest called Caatinga. This study aimed to evaluate the structure and genomic diversity of umbu trees from seven locations in the Caatinga biome, distributed among four Brazilian states. Using genotyping-by-sequencing (GBS), a total of 5,336 SNPs were obtained, of which 250 showed outlier behavior. Therefore, 5,086 neutral SNPs were used for population structure and genetic diversity analyses. Both discriminant analysis of principal components (DAPC) and neighbor-joining cluster analyses classified the accessions into four groups, with a genetic structure observed among groups, disagreeing with our initial hypothesis of low genetic structure between locations. Isolation by distance (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.974; p\u0026thinsp;=\u0026thinsp;0.0015) was detected. Moderate to high levels of genetic diversity were found, with the average observed heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eO\u003c/em\u003e\u003c/sub\u003e = 0.221) higher than the expected heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003eE\u003c/em\u003e\u003c/sub\u003e = 0.199) and with negative inbreeding coefficient (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e) values. Most genetic variation was found within locations, although high diversity between locations (22.1%) was observed. The results obtained are important for understanding the levels and distribution of genetic variation, suggesting that most locations are priorities for conservation actions, contributing with different alleles to the species' gene pool in Brazil.\u003c/p\u003e","manuscriptTitle":"SNP-based analysis reveals high genetic structure and diversity in umbu tree (Spondias tuberosa Arruda), a native and endemic species of the Caatinga biome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-17 06:40:59","doi":"10.21203/rs.3.rs-4253622/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-24T13:48:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-24T13:37:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-23T17:36:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"ad0565b7-3baa-4d16-a4d6-eabc2722f24a","date":"2024-04-14T17:38:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"ffc116e4-568b-46cf-9755-81cfd2e0564e","date":"2024-04-13T23:43:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-12T14:15:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-12T13:44:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-12T13:44:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genetic Resources and Crop Evolution","date":"2024-04-11T16:30:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0b7b86d6-f0bc-4654-9410-69c0edcc135b","owner":[],"postedDate":"April 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-25T00:39:33+00:00","versionOfRecord":{"articleIdentity":"rs-4253622","link":"https://doi.org/10.1007/s10722-024-02024-0","journal":{"identity":"genetic-resources-and-crop-evolution","isVorOnly":false,"title":"Genetic Resources and Crop Evolution"},"publishedOn":"2024-05-24 00:39:33","publishedOnDateReadable":"May 24th, 2024"},"versionCreatedAt":"2024-04-17 06:40:59","video":"","vorDoi":"10.1007/s10722-024-02024-0","vorDoiUrl":"https://doi.org/10.1007/s10722-024-02024-0","workflowStages":[]},"version":"v1","identity":"rs-4253622","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4253622","identity":"rs-4253622","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}